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FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control.

Jianqing Fan1, Yuan Ke2, Qiang Sun3

  • 1Honorary Professor, School of Data Science, Fudan University, Shanghai, China and Frederick L. Moore '18 Professor of Finance, Department of Operations Research and Financial Engineering, Princeton University, NJ 08544 (jqfan@princeton.edu).

Journal of the American Statistical Association
|October 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces FarmTest, a robust multiple testing procedure. It accurately controls the false discovery proportion (FDP) even with correlated, heavy-tailed data, outperforming existing methods.

Keywords:
Factor adjustmentFalse discovery proportionHuber lossLarge-scale multiple testingRobustness

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Area of Science:

  • Statistics
  • Bioinformatics
  • Data Science

Background:

  • Large-scale multiple testing is crucial in genomics, medical imaging, and finance.
  • Conventional methods struggle with correlated, heavy-tailed data and often assume joint normality.
  • Ignoring data structure leads to inefficient or inconsistent false discovery proportion (FDP) estimation.

Purpose of the Study:

  • To propose a novel Factor-Adjusted Robust Multiple Testing (FarmTest) procedure.
  • To ensure accurate control of the FDP in large-scale simultaneous inference.
  • To improve statistical power and estimation consistency.

Main Methods:

  • Developed a robust factor adjustment strategy for multiple testing.
  • Established conditions for consistent FDP estimation.
  • Derived a deviation inequality for robust U-type covariance estimators.

Main Results:

  • FarmTest demonstrates superior FDP control and power, especially with heavy-tailed data.
  • Robust factor adjustments are critical for performance.
  • The method shows advantages over state-of-the-art techniques in simulations.

Conclusions:

  • FarmTest offers a robust and powerful solution for large-scale multiple testing.
  • The procedure is effective even when standard assumptions are violated.
  • An R-package, FarmTest, is available for practical application.